Objective: The purpose of this study was to optimize the number and positions of foot pressure sensors using the reliability analysis of the center of pressure (COP) in smart shoes. Background: Foot pressure can be different according to foot region, and it is important which region of the foot pres...
Objective: The purpose of this study was to optimize the number and positions of foot pressure sensors using the reliability analysis of the center of pressure (COP) in smart shoes. Background: Foot pressure can be different according to foot region, and it is important which region of the foot pressure needs to be measured. Method: Thirty adults (age: $20.5{\pm}1.8years$, body weight: $71.4{\pm}6.5kg$, height: $1.76{\pm}0.04m$) participated in this study. The foot pressure data were collected using the insole of Pedar-X system (Novel GmbH, USA) with a sampling frequency of 100Hz during 1.3m/s speed walking on the treadmill (Instrumented treadmill, Bertec, USA). The intraclass correlation coefficients (ICC) were calculated between the COP positions using 4, 5, 6, 7, 8, and 99 sensors, while one-way repeated measure ANOVA was performed between the standard deviation (SD) of the COP positions. Results: The medio-lateral (M/L) COP position using 99 sensors was positively correlated with the M/L COP positions using 6, 7, and 8 sensors; however, it was not correlated with the M/L COP positions using 4 and 5 sensors during landing phase (1~4%) (p<.05). The antero-posterior (A/P) COP position using 99 sensors was positively correlated with the A/P COP positions using 4, 5, 6, 7, and 8 sensors (p<.05). The SD of the COP position using 99 sensors was smaller than the SD of the M/L COP positions using 4, 5, 6, 7, and 8 sensors (p<.05). Conclusion: Based on our findings, it is desirable to arrange at least 6 sensors in smart shoes. Application: The study of optimizing the number and positions of foot pressure sensors would contribute to developing more effective smart shoes using foot pressure technology.
Objective: The purpose of this study was to optimize the number and positions of foot pressure sensors using the reliability analysis of the center of pressure (COP) in smart shoes. Background: Foot pressure can be different according to foot region, and it is important which region of the foot pressure needs to be measured. Method: Thirty adults (age: $20.5{\pm}1.8years$, body weight: $71.4{\pm}6.5kg$, height: $1.76{\pm}0.04m$) participated in this study. The foot pressure data were collected using the insole of Pedar-X system (Novel GmbH, USA) with a sampling frequency of 100Hz during 1.3m/s speed walking on the treadmill (Instrumented treadmill, Bertec, USA). The intraclass correlation coefficients (ICC) were calculated between the COP positions using 4, 5, 6, 7, 8, and 99 sensors, while one-way repeated measure ANOVA was performed between the standard deviation (SD) of the COP positions. Results: The medio-lateral (M/L) COP position using 99 sensors was positively correlated with the M/L COP positions using 6, 7, and 8 sensors; however, it was not correlated with the M/L COP positions using 4 and 5 sensors during landing phase (1~4%) (p<.05). The antero-posterior (A/P) COP position using 99 sensors was positively correlated with the A/P COP positions using 4, 5, 6, 7, and 8 sensors (p<.05). The SD of the COP position using 99 sensors was smaller than the SD of the M/L COP positions using 4, 5, 6, 7, and 8 sensors (p<.05). Conclusion: Based on our findings, it is desirable to arrange at least 6 sensors in smart shoes. Application: The study of optimizing the number and positions of foot pressure sensors would contribute to developing more effective smart shoes using foot pressure technology.
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문제 정의
This study was conducted to optimize the number and positions of the pressure sensors required for smart shoes development using foot pressure sensors during gait analysis. To achieve the goal, a reliability analysis was carried out between the COP position estimated with 99 sensors' pressure values and those calculated with 4, 5, 6, 7, and 8 sensors.
To achieve the goal, a reliability analysis was carried out between the COP position estimated with 99 sensors' pressure values and those calculated with 4, 5, 6, 7, and 8 sensors. Using the analysis, this study aimed to present the optimal number and positions of the pressure sensors. Through the comparison of SD of COP position between each sensor condition, this study tried to analyze consistency of data.
제안 방법
The Pedar X system (Novel GmbH, USA) insoles embedded with 99 sensors to measure foot pressure distribution was inserted into shoes as shown in Figure 1. After wearing the shoes, the participants in this study conducted warm-up trials for 5 minutes in order to walk naturally on the treadmill. The experiment was carried out with a walking speed of 1.
First, upon the observation of the positions of the sensors used for the analysis, they were set to 8 regions of the foot, and the reliability and consistency were analyzed by estimating the COP positions with the pressure values of the corresponding sensors. The sensor positions were decided according to the order of foot pressure levels in walking based on the functional anatomical positions (Pappas et al.
In addition, limitations were revealed in precisely evaluating a human body's dynamic movements including walking, as comparisons were made through scoring the ordinal scale of positions' pressure values.
Lastly, the merit of this study is determined to be high in that the positions of sensors were decided on the basis of functional and anatomical standard considerations of gait characteristics compared with previous studies on the number and positions of pressure sensors, and in that the optimization of the number of sensors was conducted through reliability and consistency analyses with respect to the COP position estimated with 99 sensors. Therefore, this investigation would provide objective and meaningful information in order to monitor more accurate gait parameters such as the types and levels of activity using pressure sensors in the shoe.
After wearing the shoes, the participants in this study conducted warm-up trials for 5 minutes in order to walk naturally on the treadmill. The experiment was carried out with a walking speed of 1.3m/s on the treadmill (Instrumented treadmill, Bertec, USA) used in the general gait analysis (Doke et al., 2004) (Figure 2). The 10 strides were selected for each walking condition, and the data collection was set to a sampling frequency 100Hz.
Lastly, the merit of this study is determined to be high in that the positions of sensors were decided on the basis of functional and anatomical standard considerations of gait characteristics compared with previous studies on the number and positions of pressure sensors, and in that the optimization of the number of sensors was conducted through reliability and consistency analyses with respect to the COP position estimated with 99 sensors. Therefore, this investigation would provide objective and meaningful information in order to monitor more accurate gait parameters such as the types and levels of activity using pressure sensors in the shoe.
Third, this study calculated SD on position by each sensor to examine consistency of COP data between the conditions. Since SD means dispersion on the repeated results, consistency is determined to be higher, as the value of SD is smaller.
This study aimed to present optimal pressure sensor positions to develop smart shoes embedded with precise walking analysis function through a COP position's reliability analysis according to foot pressure sensor position during walking.
Through smart shoes development, users' levels of exercise and activity amount can be measured, and accurate gait characteristics can be determined. This study was performed to optimize the number and positions of foot pressure sensors required for smart shoes development, with reliability and consistency analyses being undertaken. The conclusion of this study is as follows:
This study was undertaken to present optimal pressure sensor positions to develop smart shoes using precise walking analysis function through a COP position's reliability analysis according to foot pressure sensor position in walking.
Using the analysis, this study aimed to present the optimal number and positions of the pressure sensors. Through the comparison of SD of COP position between each sensor condition, this study tried to analyze consistency of data.
To achieve the goal, a reliability analysis was carried out between the COP position estimated with 99 sensors' pressure values and those calculated with 4, 5, 6, 7, and 8 sensors.
대상 데이터
30 male adults in their 20s (age: 20.5±1.8 years; height: 176.2±4.2cm; weight: 71.4±6.5kg) without orthopedic disease history, participated in this study.
Based on 425 matrix (17×52), this study measured pressure by foot position using the Pedar-X system insole where 99 sensors place (Figure 3).
, 2004) (Figure 2). The 10 strides were selected for each walking condition, and the data collection was set to a sampling frequency 100Hz.
데이터처리
To achieve the goal, the ICC between the COP position estimated with 99 sensors' pressure values and the COP positions using 4, 5, 6, 7, and 8 sensors were calculated. Also, a repeated measure one-way ANOVA on standard deviation of COP position by each sensor placement was conducted.
ICC were calculated between the COP position using 99 sensors and the COP positions using 4, 5, 6, 7, and 8 sensors. Consistency was analyzed through repeated measure one-way ANOVA on the standard deviation of COP position from each sensor placements. In doing so, SPSS Ver.
Table 5 shows the results of repeated measure one-way ANOVA by calculating the SD of COP position by sensor. The SD of M/L COP positions in the right foot stance phase showed a statistically significant result at F=49.
To examine the COP position closest to the COP position estimated with 99 sensors' pressure values, a reliability analysis was conducted using the intraclass correlation coefficients (ICC).
성능/효과
000. According to the post hoc test result, the SD of A/P COP position calculated with 99 sensors was smaller than the SD of those calculated with 4, 5, 6, 7, and 8 sensors, and a statistically significant difference was shown. The SD of A/P COP position calculated with 4 sensors was larger than the SD of those calculated with 6, 7, and 8 sensors, and a statistically significant result was found.
000. According to the post hoc test result, the SD of M/L COP position calculated with 99 sensors was smaller than the SD of those calculated with 4, 5, 6, 7, and 8 sensors, and a statistically significant difference was shown. The SD of M/L COP position calculated with 4 sensors was smaller than that of those calculated with 5, 6, and 8 sensors, and a statistically significant result was found.
The reason is judged to be the result of the sensor positions set at foot heel and hallux. According to the reliability analysis result on M/L COP positions, a statistically high positive correlation with statistically significant level was shown in the total stance phase from the moment when right heel strikes the ground to the moment when the end of the foot taking off the ground (1~30%) between the M/L positions estimated with 99 sensors and those estimated with 6, 7, and 8 sensors. Meanwhile, a low correlation was shown in the stance phase where right heel strikes the ground (1~4%) in terms of M/L COP positions estimated with 4 and 5 sensors, and a statistically significant result was not shown.
According to the post hoc test result, the SD of A/P COP position calculated with 99 sensors was smaller than the SD of those calculated with 4, 5, 6, 7, and 8 sensors, and a statistically significant difference was shown. The SD of A/P COP position calculated with 4 sensors was larger than the SD of those calculated with 6, 7, and 8 sensors, and a statistically significant result was found.
According to the post hoc test result, the SD of M/L COP position calculated with 99 sensors was smaller than the SD of those calculated with 4, 5, 6, 7, and 8 sensors, and a statistically significant difference was shown. The SD of M/L COP position calculated with 4 sensors was smaller than that of those calculated with 5, 6, and 8 sensors, and a statistically significant result was found. The SD of A/P COP positions in the right foot stance phase, a statistically significant result was shown at F=13.
The correlations between A/P COP position estimated with 99 sensors and the M/L COP positions estimated with 6, 7, and 8 sensors showed a statistically high positive correlation from the moment the right heel strikes to the moment the end of the foot takes off the ground (1~30%). The correlation coefficients with M/L COP positions estimated with 4 and 5 sensors showed a low correlation at the section where right heel strikes (1~4%), and did not show a statistically significant result. Meanwhile, in the A/P COP position, a high positive correlation was shown between the COP position estimated with 99 sensors and COP positions estimated with all other sensors, also showing a statistically significant result.
Tables 3 and 4 show the ICC results between the COP position estimated with 99 sensors and those estimated with 4, 5, 6, 7, and 8 sensors. The correlations between A/P COP position estimated with 99 sensors and the M/L COP positions estimated with 6, 7, and 8 sensors showed a statistically high positive correlation from the moment the right heel strikes to the moment the end of the foot takes off the ground (1~30%). The correlation coefficients with M/L COP positions estimated with 4 and 5 sensors showed a low correlation at the section where right heel strikes (1~4%), and did not show a statistically significant result.
Upon looking at M/L COP positions, the consistency of the M/L COP position estimated with 99 sensors was highest, and the consistency of M/L COP positions estimated with 4, 5, 6, 7, and 8 sensors was smaller than that. Through the result that the consistency of M/L COP position estimated with 4 sensors was relatively higher than that estimated with 4, 5, and 8 sensors, it is conjectured to be more important selecting precise sensor positions rather than increasing the number of sensors in terms of M/L COP positions. Through further studies, there is a need to analyze the optimal positions of sensors for improvement of consistency on the M/L COP positions.
To achieve the goal, the ICC between the COP position estimated with 99 sensors' pressure values and the COP positions using 4, 5, 6, 7, and 8 sensors were calculated.
Upon looking at M/L COP positions, the consistency of the M/L COP position estimated with 99 sensors was highest, and the consistency of M/L COP positions estimated with 4, 5, 6, 7, and 8 sensors was smaller than that. Through the result that the consistency of M/L COP position estimated with 4 sensors was relatively higher than that estimated with 4, 5, and 8 sensors, it is conjectured to be more important selecting precise sensor positions rather than increasing the number of sensors in terms of M/L COP positions.
후속연구
Although this study was conducted at a certain gait speed targeting adult males, there is a need to carry out optimization on the number and positions of foot pressure sensors and gait speed variation for females in a further study.
Through the result that the consistency of M/L COP position estimated with 4 sensors was relatively higher than that estimated with 4, 5, and 8 sensors, it is conjectured to be more important selecting precise sensor positions rather than increasing the number of sensors in terms of M/L COP positions. Through further studies, there is a need to analyze the optimal positions of sensors for improvement of consistency on the M/L COP positions. The consistency of A/P COP position estimated that 99 sensors was the highest, and the consistency of A/P COP position estimated with 4 sensors was the lowest.
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